Mode decomposition method based on matrix calculation

Abstract

The invention provides a mode decomposition method based on matrix calculation, and the method comprises the steps: obtaining a light spot pattern, and carrying out the pixel sampling of the light spot pattern, so as to obtain a plurality of sampling blocks; and performing mode decomposition on the plurality of sampling blocks to obtain an optimal solution. According to the method, the calculation efficiency can be greatly improved while high decomposition precision is kept, and the method has certain anti-noise performance.

Description

Technical Field

[0001] The invention relates to the technical field of optical fiber laser mode decomposition, and in particular to a mode decomposition method based on matrix calculation. Background Art

[0002] With the widespread use of technologies such as wavelength division multiplexing (WDM), polarization multiplexing (PDM), coherent reception, and multi-dimensional multi-order modulation, the transmission capacity of single-mode optical fiber has rapidly approached its Shannon limit. In order to seek a new high-speed and large-capacity optical transmission mechanism, mode division multiplexing (MDM) technology based on few-mode fiber came into being. By utilizing the orthogonality between the various modes of the optical fiber, each mode is regarded as an independent channel loading signal to form a multiple-input multiple-output (MIMO) channel to improve the system transmission capacity and spectrum efficiency.

[0003] Through reasonable design of the fiber, few-mode fiber can only stimulate and transmit a limited number of modes. Compared with single-mode fiber, MDM technology can be used to expand the transmission capacity of a single fiber; compared with multi-mode fiber, the number of modes can be controlled, and mode dispersion and crosstalk can be optimized.

[0004] Mode decomposition algorithm is a key technology for characterizing the mode coupling characteristics of optical fibers. It has important research significance by obtaining the modal weights and modal relative phase information of each mode in the optical fiber, analyzing the mode coupling changes in the FMF, and inferring the relevant beam properties from the complete optical fiber light field. In recent years, mode decomposition methods based on different principles, such as spatial and spectral domain resolved imaging (F2) method, optical correlation analysis (OCA) method, inverse matrix solution method, stochastic gradient descent (SPGD) method, convolutional neural network (CNN) method, etc., have been proposed one after another. However, the existing mode decomposition methods have poor stability, poor noise resistance, or require a lot of computing power, which severely limits their scope in practical applications. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a pattern decomposition method based on matrix calculation.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme.

[0007] In a first aspect, the present invention provides a pattern decomposition method based on matrix calculation, comprising:

[0008] Acquire a spot image, and perform pixel sampling on the spot image to obtain a plurality of sampling blocks;

[0009] Mode decomposition is performed on the multiple sampling blocks to obtain an optimal solution.

[0010] Further, performing pattern decomposition on the multiple sampling blocks to obtain an optimal solution includes:

[0011] Determine a first matrix according to the total intensity vector of the sampling block and field distribution matrices in different modes;

[0012] The amplitude coefficient and the phase coefficient are obtained based on the first matrix, and the objective function is constructed using the amplitude coefficient and the phase coefficient. The objective function is solved to obtain an optimal solution.

[0013] Further, determining the first matrix according to the total intensity vector of the sampling block and the field distribution matrix in different modes includes:

[0014] Get the total intensity vector Γ of the sampling block α and the field distribution matrix T under different modes α :

[0015]

[0016] v is the polarization direction, including the x polarization direction and the y polarization direction, s, w are the LP mode excitation order, represents the product of the distribution of the LP01 mode in the x polarization direction corresponding to the first pixel point in the b-th sampling block and the LP11 mode in the x polarization direction, B is the total number of sampling blocks, α is the polarization angle, and β is the set of coordinate points of all sampling blocks;

[0017] According to the total intensity vector Γ α and the field distribution matrix T α The relationship between the two is calculated to obtain the first matrix R α :

[0018] Γ α =T α R α .

[0019] Furthermore, the amplitude coefficient includes a first amplitude coefficient A x,n and the second amplitude coefficient A y,n , the phase coefficient includes a first phase coefficient and a second phase coefficient, and the amplitude coefficient and the phase coefficient are obtained by solving the first matrix, including:

[0020] Based on the first matrix R α The first N terms are calculated to obtain the first amplitude coefficient A x,n and the second amplitude coefficient A y,n :

[0021]

[0022] Where N is the maximum number of modes supported in the polarization direction, A v,n Including the first amplitude coefficient A x,nand the second amplitude coefficient A y,n ;

[0023] Using R (0) and the first amplitude coefficient A in the x-polarization direction x,n Solve to obtain the first phase coefficient θ x,n :

[0024]

[0025] Using Γ 45 Solve for the second phase coefficient θ y,1 :

[0026]

[0027] In the formula, Represents the product of the LP01 mode in the x-polarization direction and the LP11 mode field distribution in the y-polarization direction corresponding to the first pixel point in the b-th sampling block.

[0028] Furthermore, the use of R (0) and the first amplitude coefficient A in the x-polarization direction x,n Solve to obtain the first phase coefficient θ x,n ,include:

[0029] Assume θ x,2 ∈[0,π], then the first phase coefficient is:

[0030]

[0031] When n>2, the first phase coefficient is:

[0032]

[0033] Further, the use of Γ 45 Solve for the second phase coefficient θ y,1 ,include:

[0034]

[0035] Solve for θ by R(90) y,n (n≥2), where θ y,2 It can be expressed as:

[0036]

[0037] When n>2, the second phase coefficient θ y,n for:

[0038]

[0039] Further, constructing an objective function using the amplitude coefficient and the phase coefficient includes:

[0040] The objective function L is constructed according to the first amplitude coefficient, the second amplitude coefficient, the first phase coefficient and the second phase coefficient:

[0041]

[0042] Wherein, i=1…8, i is the number of mode coefficient solutions.

[0043] In a second aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0044] In a third aspect, the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above method when executed by a processor.

[0045] In a fourth aspect, the present invention further provides a computer program product, comprising a computer program, wherein the computer program implements the above method when executed by a processor.

[0046] Beneficial effects of the present invention: The matrix-based pattern decomposition method provided by the present invention samples the spot image at pixel points and performs pattern decomposition on the sampling blocks, thereby converting the inherent nonlinear pattern decomposition problem into two parts: a cumbersome linear part and a simple nonlinear part, so that the coefficient solution becomes a matrix multiplication calculation, which greatly improves the speed of pattern decomposition. Compared with the input of the entire spot image, the number of multiplications is significantly reduced while maintaining the same accuracy, greatly improving the computational efficiency. The present invention can greatly increase its computational efficiency while maintaining high decomposition accuracy. For pattern decomposition situations with different signal-to-noise ratios, it can be found that increasing the number of sampling points within a certain range can reduce the error of pattern decomposition. At the same time, compared with other pattern decomposition algorithms, the algorithm also shows certain noise resistance.

[0047] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 A schematic flow chart of a pattern decomposition method based on matrix calculation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0051] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "an" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0052] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0053] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0054] Example 1

[0055] See also Figure 1 , a pattern decomposition method based on matrix calculation, comprising the following steps:

[0056] S101, obtaining a light spot map, and performing pixel sampling on the light spot map to obtain a sampling block.

[0057] Specifically, pixels are randomly sampled from the collected spot image and formed into a sampling block of any shape and size. There can be a total of B overlapping sampling blocks with L pixels. A sampling block βb It can be expressed mathematically as:

[0058]

[0059] in, Represents the coordinate position of the lth pixel in the bth sampling block, b=1…B, l=1…L.

[0060] S102: Perform pattern decomposition on the multiple sampling blocks to obtain an optimal solution.

[0061] In this step, the first matrix is ​​determined according to the total intensity vector of the sampling block and the field distribution matrices under different modes.

[0062] Get the total intensity vector Γ of the sampling block α and the field distribution matrix T under different modes α :

[0063]

[0064] In the formula, Represents the coordinate position of the lth pixel in the bth sampling block, v is the polarization direction, including the x polarization direction and the y polarization direction, s, w are the LP mode excitation order, represents the product of the LP01 mode in the x polarization direction corresponding to the first pixel point in the b-th sampling block and the LP11 mode distribution in the x polarization direction, β is the set of coordinate points of all sampling blocks, and N is the maximum number of modes supported in the polarization direction.

[0065] According to the total intensity vector Γ α and the field distribution matrix T α The relationship between the two is calculated to obtain the first matrix R α :

[0066] Γ α =T α R α .

[0067] It should be noted that when α=0, v=x and v=y for ɑ=90, R α is a vector of length N(N+1) / 2, defined as:

[0068]

[0069] The amplitude coefficient and the phase coefficient are obtained based on the first matrix, and the objective function is constructed using the amplitude coefficient and the phase coefficient. The objective function is solved to obtain an optimal solution.

[0070] Among them, the amplitude coefficient includes the first amplitude coefficient A x,n and the second amplitude coefficient A y,n , the phase coefficients include a first phase coefficient θ x,n and the second phase coefficient θ y,n .

[0071] In this step, based on the first matrix R α The first N terms are calculated to obtain the first amplitude coefficient A x,n and the second amplitude coefficient A y,n :

[0072]

[0073] Where N is the maximum number of modes supported in the polarization direction, A v,n Including the first amplitude coefficient A x,n and the second amplitude coefficient A y,n .

[0074] Using M (0) and the first amplitude coefficient A in the x-polarization direction x,n Solve to obtain the first phase coefficient θ x,n :

[0075]

[0076] Using Γ 45 Solve for the second phase coefficient θ y,1 :

[0077] 2Γ 45 =PQ+Γ 0 +Γ 90

[0078]

[0079] In the formula, Represents the product of the LP01 mode in the x-polarization direction and the LP11 mode field distribution in the y-polarization direction corresponding to the first pixel point in the b-th sampling block.

[0080]

[0081] Wherein, the use of R (0) and the first amplitude coefficient A in the x-polarization direction x,n Solve to obtain the first phase coefficient θ x,n ,include:

[0082] Assume θ x,2 ∈[0,π], then the first phase coefficient is:

[0083]

[0084] When n>2, the first phase coefficient is:

[0085]

[0086] Among them, if θ x,1 =0, then:

[0087]

[0088] The utilization of Γ 45 Solve for the second phase coefficient θ y,1 ,include:

[0089]

[0090] Solve for θ by R(90) y,n (n≥2), where θ y,2 It can be expressed as:

[0091]

[0092] When n>2, the phase coefficient θ y,n for:

[0093]

[0094] The constructing the objective function by using the amplitude coefficient and the phase coefficient comprises:

[0095] The objective function is constructed according to the first amplitude coefficient, the second amplitude coefficient, the first phase coefficient and the second phase coefficient:

[0096]

[0097] Wherein, i=1…8, i is the number of mode coefficient solutions.

[0098] The mode decomposition method based on matrix calculation provided by the embodiment of the present invention and the mode decomposition algorithm based on sampling blocks can greatly increase its operation efficiency while maintaining high decomposition accuracy. For mode decomposition situations with different signal-to-noise ratios, it can be found that increasing the number of sampling points within a certain range can reduce the error of mode decomposition. At the same time, compared with other mode decomposition algorithms, the algorithm also shows certain anti-noise performance.

[0099] Example 2

[0100] On the basis of Embodiment 1, this Embodiment 2 provides a pattern decomposition device based on matrix calculation, and the pattern decomposition device based on matrix calculation corresponds to the above-mentioned pattern decomposition based on matrix calculation, and specifically includes:

[0101] A sampling block acquisition module is used to acquire a light spot diagram and perform pixel sampling on the light spot diagram to obtain a sampling block;

[0102] The mode decomposition module is used to perform mode decomposition on the multiple sampling blocks to obtain an optimal solution.

[0103] For specific details, please refer to the description of the pattern decomposition method based on matrix calculation, which will not be repeated here.

[0104] Example 3

[0105] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, the processor calls the program instructions to execute a pattern decomposition method based on matrix calculation, and the method includes the following process steps:

[0106] Acquire a spot image, and perform pixel sampling on the spot image to obtain a plurality of sampling blocks;

[0107] Mode decomposition is performed on the multiple sampling blocks to obtain an optimal solution.

[0108] Example 4

[0109] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a pattern decomposition method based on matrix calculation is implemented. The method includes the following process steps:

[0110] Acquire a spot image, and perform pixel sampling on the spot image to obtain a plurality of sampling blocks;

[0111] Mode decomposition is performed on the multiple sampling blocks to obtain an optimal solution.

[0112] Example 5

[0113] Embodiment 5 of the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, a pattern decomposition method based on matrix calculation is implemented. The method includes the following process steps:

[0114] Acquire a spot image, and perform pixel sampling on the spot image to obtain a plurality of sampling blocks;

[0115] Mode decomposition is performed on the multiple sampling blocks to obtain an optimal solution.

[0116] In summary, the matrix calculation-based pattern decomposition method provided by the embodiment of the present invention samples the spot image at pixel points and performs pattern decomposition on the sampling block, thereby converting the inherent nonlinear pattern decomposition problem into two parts: a cumbersome linear part and a simple nonlinear part, so that the coefficient solution becomes a matrix multiplication calculation, which greatly improves the speed of pattern decomposition. Compared with the input of the entire spot image, the number of multiplications is significantly reduced while maintaining the same accuracy, which greatly improves the computational efficiency. The present invention can greatly increase its computational efficiency while maintaining high decomposition accuracy. For pattern decomposition situations with different signal-to-noise ratios, it can be found that increasing the number of sampling points within a certain range can reduce the error of pattern decomposition. At the same time, compared with other pattern decomposition algorithms, the algorithm also shows certain noise resistance.

[0117] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0118] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the method or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The method and system embodiments described above are merely schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0119] The above are only preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A pattern decomposition method based on matrix calculation, characterized in that: include: Acquire a spot image, and perform pixel sampling on the spot image to obtain a plurality of sampling blocks; Mode decomposition is performed on the multiple sampling blocks to obtain an optimal solution.

2. The method according to claim 1, characterized in that The performing pattern decomposition on the plurality of sampling blocks to obtain an optimal solution comprises: Determine a first matrix according to the total intensity vector of the sampling block and field distribution matrices in different modes; The amplitude coefficient and the phase coefficient are obtained based on the first matrix, and the objective function is constructed using the amplitude coefficient and the phase coefficient. The objective function is solved to obtain an optimal solution.

3. The method according to claim 2, characterized in that The determining of the first matrix according to the total intensity vector of the sampling block and the field distribution matrix under different modes includes: Get the total intensity vector Γ of the sampling block α and the field distribution matrix Γ under different modes α : In the formula, Represents the coordinate position of the lth pixel in the bth sampling block, v is the polarization direction, including the x polarization direction and the y polarization direction, s, w are the LP mode excitation order, represents the product of the distribution of the LP01 mode in the x polarization direction corresponding to the first pixel point in the b-th sampling block and the LP11 mode in the x polarization direction, B is the total number of sampling blocks, α is the polarization angle, and β is the set of coordinate points of all sampling blocks; According to the total intensity vector Γ α and the field distribution matrix T α The relationship between the two is calculated to obtain the first matrix R α : C α =T α R α 。 4. The method according to claim 3, characterized in that The amplitude coefficients include a first amplitude coefficient A x，n and the second amplitude coefficient A y，n , the phase coefficient includes a first phase coefficient and a second phase coefficient, and the amplitude coefficient and the phase coefficient are obtained by solving the first matrix, including: Based on the first matrix R α The first N terms are calculated to obtain the first amplitude coefficient A x，n and the second amplitude coefficient A y，n : Where N is the maximum number of modes supported in the polarization direction, A v，n Including the first amplitude coefficient A x，n and the second amplitude coefficient A y，n ; Using R (0) and the first amplitude coefficient A in the x-polarization direction x，n Solve to obtain the first phase coefficient θ x，n : Using Γ 45 Solve for the second phase coefficient θ y，1 : 2C 45 =PQ+C 0 +C 90 Q=[A x，1 A y，1 cos(θ y，1 -θ x，1 ) A x，1 A y，2 cos(θ y，2 -θ x，1 )…A x，1 A y，N cos(θ y，N -θ x， 1)…A x，N A y，N cos(θ y，N -θ x，N )] T In the formula, Represents the product of the LP01 mode in the x-polarization direction and the LP11 mode field distribution in the y-polarization direction corresponding to the first pixel point in the b-th sampling block.

5. The method according to claim 4, characterized in that The use of R (0) and the first amplitude coefficient A in the x-polarization direction x，n Solve to obtain the first phase coefficient θ x，n , include: Assume θ x,2 ∈[0,π], then the first phase coefficient is: When n>2, the first phase coefficient is:

6. The method according to claim 4, characterized in that The utilization of Γ 45 Solve for the second phase coefficient θ y,1 ,include: Solve for θ by R(90) y,n (n≥2), where θ y,2 It can be expressed as: When n>2, the second phase coefficient θ y,n for:

7. The method according to claim 4, characterized in that The constructing the objective function by using the amplitude coefficient and the phase coefficient comprises: The objective function L is constructed according to the first amplitude coefficient, the second amplitude coefficient, the first phase coefficient and the second phase coefficient: Wherein, i=1…8, i is the number of mode coefficient solutions.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The device stores a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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